Although chickpea (Cicer arietinum L.), an important food legume crop, is sensitive to salinity, considerable variation for salinity tolerance exists in the germplasm. To improve any existing cultivar, it is important to understand the genetic and physiological mechanisms underlying this tolerance.
Pushpavalli et al BMC Plant Biology (2015) 15:124 DOI 10.1186/s12870-015-0491-8 RESEARCH ARTICLE Open Access Two key genomic regions harbour QTLs for salinity tolerance in ICCV × JG 11 derived chickpea (Cicer arietinum L.) recombinant inbred lines Raju Pushpavalli1,2, Laxmanan Krishnamurthy1, Mahendar Thudi1, Pooran M Gaur1, Mandali V Rao2, Kadambot HM Siddique3, Timothy D Colmer4, Neil C Turner3,5, Rajeev K Varshney1,4 and Vincent Vadez1* Abstract Background: Although chickpea (Cicer arietinum L.), an important food legume crop, is sensitive to salinity, considerable variation for salinity tolerance exists in the germplasm To improve any existing cultivar, it is important to understand the genetic and physiological mechanisms underlying this tolerance Results: In the present study, 188 recombinant inbred lines (RILs) derived from the cross ICCV × JG 11 were used to assess yield and related traits in a soil with mM NaCl (control) and 80 mM NaCl (salinity) over two consecutive years Salinity significantly (P < 0.05) affected almost all traits across years and yield reduction was in large part related to a reduction in seed number but also a reduction in above ground biomass A genetic map was constructed using 56 polymorphic markers (28 simple sequence repeats; SSRs and 28 single nucleotide polymorphisms; SNPs) The QTL analysis revealed two key genomic regions on CaLG05 (28.6 cM) and on CaLG07 (19.4 cM), that harboured QTLs for six and five different salinity tolerance associated traits, respectively, and imparting either higher plant vigour (on CaLG05) or higher reproductive success (on CaLG07) Two major QTLs for yield in the salinity treatment (explaining 12 and 17% of the phenotypic variation) were identified within the two key genomic regions Comparison with already published chickpea genetic maps showed that these regions conferred salinity tolerance across two other populations and the markers can be deployed for enhancing salinity tolerance in chickpea Based on the gene ontology annotation, forty eight putative candidate genes responsive to salinity stress were found on CaLG05 (31 genes) and CaLG07 (17 genes) in a distance of 11.1 Mb and 8.2 Mb on chickpea reference genome Most of the genes were known to be involved in achieving osmoregulation under stress conditions Conclusion: Identification of putative candidate genes further strengthens the idea of using CaLG05 and CaLG07 genomic regions for marker assisted breeding (MAB) Further fine mapping of these key genomic regions may lead to novel gene identification for salinity stress tolerance in chickpea Keywords: Chickpea, Salinity treatment, Quantitative trait loci, Yield, Genomic region, Candidate genes * Correspondence: v.vadez@cgiar.org International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 234, Telangana State, India Full list of author information is available at the end of the article © 2015 Pushpavalli et al.; licensee BioMed Central This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated Pushpavalli et al BMC Plant Biology (2015) 15:124 Background Chickpea (Cicer arietinum L.) ranks second after common bean among the pulses that are consumed [1], and is subjected to various biotic and abiotic stresses during its life cycle The yield loss in chickpea due to salinity has been estimated to be approximately 8-10% of total global production [2] Chickpea is known to be sensitive to salinity at both the vegetative and reproductive stages [3], which affects the productivity of the crop across the chickpea growing areas [4] Despite the sensitivity of the crop to salinity, there is a large variation for salinity tolerance [5-7] In order to harness the complex phenomenon of salt tolerance, it is important to understand the genetic and physiological basis of salinity tolerance in order to improve existing crop cultivars Several studies have been carried out to understand the molecular basis of salt tolerance in other crops and quantitative trait loci (QTLs) for traits associated to salinity tolerance have been identified in cereals like bread wheat [8], barley [9], and in legumes such as Medicago truncatula [10], and soybean [11] In chickpea, the development of molecular markers in recent years has paved the way to dissect the possible underlying tolerance mechanism for various stresses [12] In chickpea, although several mapping studies have been conducted to identify loci for biotic tolerance [13] and drought tolerance [14] only two studies have reported the presence of QTLs for salinity tolerance [15,16] Till date very few major QTLs were identified for yield components governing salinity tolerance Also no major QTL was identified for yield under salinity Thus it becomes important to identify more number of additional QTLs governing salinity stress tolerance for yield and yield components that can be utilised effectively in marker-assisted genetic improvement of chickpea Till date there is no report on putative candidate genes that would confer salinity tolerance in chickpea The present study reports the analysis of the agronomical traits contributing to increasing yield under salinity, the construction of a genetic map, the use of the agronomical analysis to identify QTLs for yield’ and related traits’ salinity tolerance, and the identification of putative candidate genes using an intra-specific mapping population derived from ICCV (sensitive) and JG 11 (tolerant) Results The detailed results obtained from the unbalanced analysis of variance (ANOVA) for the phenotyping data, such as mean performance of parental lines, range of trait values (i.e., maximum and minimum mean values for each trait) across RILs, broad sense heritability values (H2), F probability values and least significant difference Page of 15 (LSD) of traits across two years and treatments, are provided in Tables and Variance analysis In both years and treatments the RILs but not the parents showed significant variation for DF (days to first flower) and DM (days to maturity) Parents showed variation for DM in the salinity treatment in both the years In 2010 with the control treatment, no significant variation was observed between the two parents for all the yield and yield-related traits whereas in the salinity treatment they differed significantly except for the stem + leaf dry weight and the harvest index (HI) (Table 1) In 2011, both the control and salinity treatments did not differentiate the parents for any traits except for filled pod number and empty pod number in the control treatment (Table 2) The combined unbalanced ANOVA on two years data, for both of the treatments revealed that the traits DF, DM and 100-seed weight were significantly influenced by both genotype and environment, but largely affected by the genetic potential rather than the environment (larger F statistic value for the genotype than for the genotype × year component of the variance) All the other traits were influenced significantly by the genotype, but not by the environment component (Additional file 3: Table S3) Heritability Heritability estimates were categorized into low (5-10%), medium (10-30%), high (30-60%) and very high (>60%) according to a previous report [17] In 2010 in the control treatment, the broad-sense heritability (H2) of DF, DM, HI and 100-seed weight was high, whereas all other yield and yield-related traits had medium heritability (Table 2) In the salinity treatment, the heritability of DF, DM, 100-seed weight, stem + leaf weight was high, whereas heritability of ADM (above ground dry matter), yield, pod number, seed number and HI had medium heritability values In 2011, in the control treatment, the traits DF, DM and 100-seed weight had high heritability values, whereas all other traits had medium heritability values (Table 2) In salinity treatment, the traits ADM and yield had medium heritability, whereas all other traits had high to very high heritability values (Table 2) In summary, the phenological traits had high, whereas the yield and yield-related traits had moderate-to-high, heritability values in the salinity treatment Relationships of yield and yield-related traits variables The seed yield in the salinity treatment correlated significantly to control treatment in both the years (R2 = 0.23; R2 = 0.21) Similarly, means of all other traits in the salinity treatment significantly correlated with the control mean Control, 2010 Trait Days to flower Days to maturity Above ground dry matter (g plant -1) Yield (g plant -1) Pod number plant -1 Seed number plant -1 Stem + leaf weight (g plant -1) Harvest Index 100-seed weight (g) ICCV (SS) 31 84 22.47 10.86 41.43 41.78 11.61 0.48 25.93 JG 11 (ST) 33 78 24.34 14.18 54.52 60.01 10.16 0.59 23.84 Variation in RILs 23-50 73-99 9.67- 37.35 3.14-18.55 13.97-77.84 27.17-85.21 3.47-19.04 0.18-0.88 14.40-41.58 F Probability